DeepSeek-V3 Core Architecture and Its Training Techniques in Detail
Jing Dai · 2025
This chapter explores the core architectural innovations and training methodologies of DeepSeek-V3, a large-scale Mixture of Experts (MoE) model. It introduces the MoE framework’s dynamic routing, where only a subset of expert networks is activated per inference step, significantly reducing computation while maintaining high model capacity. This chapter highlights the integration of FP8 mixed-precision training to lower memory usage and accelerate computation. It also details the model’s distributed training strategy, including load balancing and efficient GPU communication. Through these optimizations, DeepSeek-V3 achieves a balance between scale and efficiency, enabling high performance on long-context tasks with reduced training cost and resource consumption.